This research aims to develop an AI driven multi disease framework for early detection and risk stratification of chronic diseases common in the elderly such as diabetes, cardiovascular diseases, and neurodegenerative conditions by integrating data from multimodal healthcare sources. By leveraging large-scale biomedical data from the UK Biobank, the research will help identify high-risk individuals and support precision medicine strategies that can improve health outcomes and reduce long-term healthcare costs.
Scientific Rationale:
Chronic diseases in elderly individuals often co-occur, and existing diagnostic pathways are inadequate in addressing comorbidities holistically. Recent advancements in machine learning and IoT-based sensing provide an opportunity to develop predictive models that integrate multi-modal data. The UK Biobank offers a unique, richly annotated dataset ideal for training and validating these models due to its extensive genotypic, phenotypic, and imaging data.
Aims and Objectives:
1. Develop an AI driven healthcare framework for comprehensive prediction of multiple chronic disorders in elderly patients and model their progression over time.
2. Identify key biomarkers and risk patterns to support early intervention and monitoring by integrating genomic, biochemical, lifestyle, and imaging data to enhance prediction accuracy.
3. Optimize AI model for real time deployment and develop a personalized health management system.
4. Compare the model performance against traditional methods and validate the AI-IoT framework to assess its clinical impact.